Download Advances in Data Mining. Applications and Theoretical by Rainer Schmidt, Heike Weiss, Georg Fuellen (auth.), Petra PDF

By Rainer Schmidt, Heike Weiss, Georg Fuellen (auth.), Petra Perner (eds.)

This e-book constitutes the refereed court cases of the twelfth business convention on info Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised complete papers offered have been conscientiously reviewed and chosen from ninety seven submissions. The papers are prepared in topical sections on info mining in drugs and biology; facts mining for power undefined; information mining in site visitors and logistic; facts mining in telecommunication; info mining in engineering; conception in info mining; concept in information mining: clustering; thought in information mining: organization rule mining and choice rule mining.

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Extra resources for Advances in Data Mining. Applications and Theoretical Aspects: 12th Industrial Conference, ICDM 2012, Berlin, Germany, July 13-20, 2012. Proceedings

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1 Introduction In order to describe objects or phenomena in real-world applications, usually, a relatively large set of attributes (or features) are necessary. , prices on the stock market, temperature and humidity of the air, blood pressure or pulse of a person, etc. , how they change their values, are almost as important as (or sometimes even more important than) the current values of the attributes. Therefore, we need to keep track of the changes of those values which results in very large collections of data.

For example, in Figure 1, P1 = {Humidity, Pressure}, P2 = { Temperature, Wind (velocity), Wind (direction), Radiation, Outlook } and the corresponding matrices M1 and M2 are shown in the bottom left and bottom right of the Figure. We can easily see that the original matrix can be reconstructed from the decomposition described above, and therefore, instead of the original matrix M , one can use this decomposition to calculate the results of search and analytic queries. In this paper, we target the problem of finding a decomposition so that the required storage space is minimized.

These papers clearly demonstrate that CBR-HS papers can have an impact as high and even higher as those in the CBR conference literature, where the top ranked papers receive 137, 117, 92, and 82 citations on Google Scholar. None of these CBR-HS papers clearly label themselves as CBR papers, and we may wonder whether this is not in part an explanation for their success. They present their concepts and ideas more in cognitive terms understandable to any researcher in biomedical or artificial intelligence domains, which probably contributes to making them attractive to a broader audience.

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